Stephanie van de Ven, MD, PhD

Bio

Bio

As Deputy Director of the Canary Center at Stanford for Cancer Early Detection I broadly oversee its operations and research programs. The Canary Center is focused on developing in vitro and in vivo tools for early cancer detection and its research spans the areas of biomarker discovery, development of molecular imaging agents, development of new diagnostic and imaging devices, and mathematical modeling. In my position I facilitate the clinical translation of cancer diagnostic tools and I enable innovative interdisciplinary research. My research expertise includes leading phase I-II clinical trials to evaluate a newly developed optical breast imaging system in combination with a novel imaging agent. I gained valuable experience in clinical translation of medical devices and in testing new imaging agents for the first time in patients. My training as a Radiology resident was instrumental in my decision to focus on cancer early detection research, because it clearly confronted me with the problem that most cancer patients are being diagnosed too late. I expanded my knowledge on biomarker research by developing proteomics assays during my postdoctoral fellowship at Stanford, in conjunction with my continued work in optical and photoacoustic molecular imaging. In my current role, I work with the faculty of the Canary Center and the Molecular Imaging Program at Stanford, and am committed to advancing cancer research by applying my medical training, clinical knowledge, and research expertise to managing collaborative programs and contribute to the success of the Center and its faculty.

Abstract

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments, and supervised classification of images into pre-defined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification, and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals, or do this with separate \textit{post hoc} procedures.} \rev{We introduce \textit{spatial shrunken centroids}, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified and normalized spectral features, and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class, and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset, and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package \textbf{Cardinal}, as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.

Abstract

Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.o.vitek@neu.eduThe code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.

Abstract

We aimed to develop a multivariable model for prediction of underestimated invasiveness in women with ductal carcinoma in situ at stereotactic large core needle biopsy, that can be used to select patients for sentinel node biopsy at primary surgery.From the literature, we selected potential preoperative predictors of underestimated invasive breast cancer. Data of patients with nonpalpable breast lesions who were diagnosed with ductal carcinoma in situ at stereotactic large core needle biopsy, drawn from the prospective COBRA (Core Biopsy after RAdiological localization) and COBRA2000 cohort studies, were used to fit the multivariable model and assess its overall performance, discrimination, and calibration.348 women with large core needle biopsy-proven ductal carcinoma in situ were available for analysis. In 100 (28.7%) patients invasive carcinoma was found at subsequent surgery. Nine predictors were included in the model. In the multivariable analysis, the predictors with the strongest association were lesion size (OR 1.12 per cm, 95% CI 0.98-1.28), number of cores retrieved at biopsy (OR per core 0.87, 95% CI 0.75-1.01), presence of lobular cancerization (OR 5.29, 95% CI 1.25-26.77), and microinvasion (OR 3.75, 95% CI 1.42-9.87). The overall performance of the multivariable model was poor with an explained variation of 9% (Nagelkerke's R(2)), mediocre discrimination with area under the receiver operating characteristic curve of 0.66 (95% confidence interval 0.58-0.73), and fairly good calibration.The evaluation of our multivariable prediction model in a large, clinically representative study population proves that routine clinical and pathological variables are not suitable to select patients with large core needle biopsy-proven ductal carcinoma in situ for sentinel node biopsy during primary surgery.

Abstract

To determine whether optical imaging can be used for in vivo therapy response monitoring as an alternative to radionuclide techniques. For this, we evaluated the known Her2 response to 17-dimethylaminoethylamino-17-demethoxygeldanamycin hydrochloride (17-DMAG) treatment, an Hsp90 inhibitor.After in vitro 17-DMAG treatment response evaluation of MCF7 parental cells and 2 HER2-transfected clones (clone A medium, B high Her2 expression), we established human breast cancer xenografts in nude mice (only parental and clone B) for in vivo evaluation. Mice received 120 mg/kg of 17-DMAG in 4 doses at 12-hour intervals intraperitonially (n = 14) or PBS as carrier control (n = 9). Optical images were obtained both pretreatment (day 0) and posttreatment (day 3, 6, and 9), always 5 hours postinjection of 500 pmol of anti-Her2 Affibody-AlexaFluor680 via tail vein (with preinjection background subtraction). Days 3 and 9 in vivo optical imaging signal was further correlated with ex vivo Her2 levels by Western blot after sacrifice.Her2 expression decreased with 17-DMAG dose in vitro. In vivo optical imaging signal was reduced by 22.5% in clone B (P = 0.003) and by 9% in MCF7 parental tumors (P = 0.23) 3 days after 17-DMAG treatment; optical imaging signal recovered in both tumor types at days 6 to 9. In the carrier group, no signal reduction was observed. Pearson correlation of in vivo optical imaging signal with ex vivo Her2 levels ranged from 0.73 to 0.89.Optical imaging with an affibody can be used to noninvasively monitor changes in Her2 expression in vivo as a response to treatment with an Hsp90 inhibitor, with results similar to response measurements in positron emission tomography imaging studies.

Abstract

Using scatterplots of 2 or 3 parameters, diffuse optical tomography and fluorescence imaging are combined to improve detectability of breast lesions. Small or low contrast phantom-lesions that were missed in the optical and fluorescence images were detected in the scatterplots. In patient measurements, all tumors were visible and easily differentiated from artifacts and areolas in the scatterplots. The different rate of intake and wash out of the fluorescent contrast agent in the healthy versus malignant tissues was also observed in the scatterplot: this information can be used to discriminate malignant lesion from normal structures.

Abstract

The aim of the study was to determine the feasibility of using a clinical optical breast scanner with molecular imaging strategies based on modulating light transmission.Different concentrations of single-walled carbon nanotubes (SWNT; 0.8-20.0 nM) and black hole quencher-3 (BHQ-3; 2.0-32.0 µM) were studied in specifically designed phantoms (200-1,570 mm(3)) with a clinical optical breast scanner using four wavelengths. Each phantom was placed in the scanner tank filled with optical matching medium. Background scans were compared to absorption scans, and reproducibility was assessed.All SWNT phantoms were detected at four wavelengths, with best results at 684 nm. Higher concentrations (?8.0 µM) were needed for BHQ-3 detection, with the largest contrast at 684 nm. The optical absorption signal was dependent on phantom size and concentration. Reproducibility was excellent (intraclass correlation 0.93-0.98).Nanomolar concentrations of SWNT and micromolar concentrations of BHQ-3 in phantoms were reproducibly detected, showing the potential of light absorbers, with appropriate targeting ligands, as molecular imaging agents for clinical optical breast imaging.

Abstract

This is the first clinical evaluation of a novel fluorescent imaging agent (Omocianine) for breast cancer detection with diffuse optical tomography (DOT).Eleven women suspected of breast cancer were imaged with DOT at multiple time points (up to 24 h) after receiving an intravenous injection of Omocianine (doses 0.01 to 0.1 mg/kg bodyweight). Breast MRI was obtained for comparison.Histopathology showed invasive cancer in ten patients and fibroadenoma in one patient. With the lowest dose of Omocianine, two of three lesions were detected; with the second dose, three of three lesions were detected; with the two highest doses, none of five lesions were detected. Lesion location on DOT showed excellent agreement with MRI. Optimal lesion-to-background signals were obtained after 8 h. No adverse events occurred.Lowest doses of Omocianine performed best in lesion detection; DOT using a low-dose fluorescent agent is feasible and safe for breast cancer visualization in patients.

Abstract

The purpose of the study was to evaluate the accuracy of 3.0-T breast MRI interpretation using manual and fully automated kinetic analyses.Manual MRI interpretation was done on an Advantage Workstation. Retrospectively, all examinations were processed with a computer-aided detection (CAD) system. CAD data sets were interpreted by two experienced breast radiologists and two residents. For each lesion automated analysis of enhancement kinetics was evaluated at 50% and 100% thresholds. Forty-nine malignant and 22 benign lesions were evaluated.Using threshold enhancement alone, the sensitivity and specificity of CAD were 97.9% and 86.4%, respectively, for the 50% threshold, and 97.9% and 90%, respectively, for the 100% threshold. Manual interpretation by two breast radiologists showed a sensitivity of 84.6% and a specificity of 68.8%. For the same two radiologists the mean sensitivity and specificity for CAD-based interpretation was 90.4% (not significant) and 81.3% (significant at p < 0.05), respectively. With one-way ANOVA no significant differences were found between the two breast radiologists and the two residents together, or between any two readers separately.CAD-based analysis improved the specificity compared with manual analysis of enhancement. Automated analysis at 50% and 100% thresholds showed a high sensitivity and specificity for readers with varying levels of experience.

Abstract

This paper presents an evaluation of a prototype diffuse optical tomography (DOT) system. Seventeen women with 18 breast lesions (10 invasive carcinomas, 2 fibroadenomas, and 6 benign cysts; diameters 13-54 mm) were evaluated with DOT and magnetic resonance imaging (MRI). A substantial fraction of the original 36 recruited patients could not be examined using this prototype due to technical problems. A region of interest (ROI) was drawn at the lesion position as derived from MRI and at the mirror image site in the contralateral healthy breast. ROIs were assessed quantitatively and qualitatively by two observers independently in two separate readings. Intra- and interobserver agreements were calculated using kappa statistics (k) and intraclass correlation coefficients (ICCs). Discriminatory values for presence of malignancy were determined by receiver operating characteristic (ROC) analyses. Intraobserver agreements were excellent (k 0.88 and 0.88; ICC 0.978 and 0.987), interobserver agreements were good to excellent (k 0.77-0.95; ICC 0.96-0.98). Discriminatory values for presence of malignancy were 0.92-0.93 and 0.97-0.99 for quantitative and qualitative ROC analysis, respectively. This DOT system has the potential to discriminate malignant from benign breast tissue in a reproducible qualitative and quantitative manner. Important technical improvements are required before this technique is ready for clinical application.

Abstract

The purpose of this study was to validate a newly developed diffuse optical tomography (DOT) system on benign cysts in the breast.Eight patients with 20 benign cysts were included. Study procedures consisted of optical breast imaging and breast magnetic resonance imaging (MRI) for comparison. A reconstruction algorithm computed three-dimensional images for each of the four near-infrared wavelengths used by our DOT system (Philips Healthcare, Best, The Netherlands). These images were combined using a spectroscopic model to assess tissue composition and lesion size.Twenty cysts were analyzed in eight patients. By using the spectroscopic information, 13 of 20 cysts (65%) were visualized with DOT, confirming their high water and low total hemoglobin content. Lesion size and location showed good agreement with MRI; Pearson correlation coefficient was 0.7 (p < 0.01).DOT can visualize benign cysts in the breast and elucidate their high water and low total hemoglobin content by spectroscopic analysis.

Abstract

This review provides a summary of the current state of optical breast imaging and describes its potential future clinical applications in breast cancer imaging. Optical breast imaging is a novel imaging technique that uses near-infrared light to assess the optical properties of breast tissue. In optical breast imaging, two techniques can be distinguished, i.e. optical imaging without contrast agent, which only makes use of intrinsic tissue contrast, and optical imaging with a contrast agent, which uses exogenous fluorescent probes. In this review the basic concepts of optical breast imaging are described, clinical studies on optical imaging without contrast agent are summarized, an outline of preclinical animal studies on optical breast imaging with contrast agents is provided, and, finally, potential applications of optical breast imaging in clinical practice are addressed. Based on the present literature, diagnostic performance of optical breast imaging without contrast agent is expected to be insufficient for clinical application. Development of contrast agents that target specific molecular changes associated with breast cancer formation is the opportunity for clinical success of optical breast imaging.